AGGRESCAN: a server for the prediction and evaluation of "hot spots" of aggregation in polypeptides

Bacterial Protein Homeostasis Disruption as a Therapeutic Intervention

Cells have evolved a complex molecular network, collectively called the protein homeostasis (proteostasis) network, to produce and maintain proteins in the appropriate conformation, concentration and subcellular localization. Loss of proteostasis leads to a reduction in cell viability, which occurs to some degree during healthy ageing, but is also the root cause of a group of diverse human pathologies. The accumulation of proteins in aberrant conformations and their aggregation into specific beta-rich assemblies are particularly detrimental to cell viability and challenging to the protein homeostasis network. This is especially true for bacteria; it can be argued that the need to adapt to their changing environments and their high protein turnover rates render bacteria particularly vulnerable to the disruption of protein homeostasis in general, as well as protein misfolding and aggregation. Targeting bacterial proteostasis could therefore be an attractive strategy for the development of novel antibacterial therapeutics. This review highlights advances with an antibacterial strategy that is based on deliberately inducing aggregation of target proteins in bacterial cells aiming to induce a lethal collapse of protein homeostasis. The approach exploits the intrinsic aggregation propensity of regions residing in the hydrophobic core regions of the polypeptide sequence of proteins, which are genetically conserved because of their essential role in protein folding and stability. Moreover, the molecules were designed to target multiple proteins, to slow down the build-up of resistance. Although more research is required, results thus far allow the hope that this strategy may one day contribute to the arsenal to combat multidrug-resistant bacterial infections.

β-Defensin from the Asian Sea Bass, Lates calcarifer: Molecular Prediction and Phylogenetic Analysis

Antimicrobial peptides (AMPs) are an important element of the innate immune system of all living organisms and serve as a barrier that safeguards the organisms against a wide range of pathogens. Fishes are proven to be a prospective source of AMPs, and β-defensins form an important family of AMPs with potent antimicrobial, chemotactic and immunomodulatory activities. The present study reports a β-defensin AMP sequence (Lc-BD) from the Asian sea bass, Lates calcarifer, a commercially important fish species in tropical and subtropical regions of Asia and the Pacific. A 202-bp cDNA fragment with an open reading frame encoding 63 amino acids (aa) was obtained from the mRNA of gill tissue by RT-PCR. The deduced aa sequence of Lc-BD possessed a signal and a mature peptide region with 20 and 43 aa residues, respectively. Lc-BD was characterized at the molecular level, and a molecular weight of 5.24 kDa and a net charge of +4.5 was predicted for the mature peptide. The molecular characterization of Lc-BD revealed the presence of three intramolecular disulphide bonds involving the six conserved cysteine residues in the sequence, and the phylogenetic analysis of Lc-BD showed a close relationship with β-defensins from fishes like Siniperca chuatsi, Argyrosomus regius, Trachinotus ovatus and Oplegnathus fasciatus.

Structural effects driven by rare point mutations in amylin hormone, the type II diabetes-associated peptide

BACKGROUND

Amylin is a 37-amino-acid peptide hormone co-secreted with insulin, which participates in glucose homeostasis. This hormone is able to aggregate in a β-sheet conformation and deposit in islet amyloids, a hallmark in type II diabetes. Since amylin is a gene-encoded hormone, this peptide has variants caused by point mutations that can impact its functions.

METHODS

Here, we analyzed the structural effects caused by S20G and G33R point mutations which, according to the 1000 Genomes Project, have frequency in East Asian and European populations, respectively. The analyses were performed by means of aggrescan server, SNP functional effect predictors, and molecular dynamics.

RESULTS

We found that both mutations have aggregation potential and cause changes in the monomeric forms when compared with wild-type amylin. Furthermore, comparative analyses with pramlintide, an amylin drug analogue, allowed us to infer that second α-helix maintenance may be related to the aggregation potential.

CONCLUSIONS

The S20G mutation has been described as pathologically related, which is in agreement with our findings. In addition, our data suggest that the G33R mutation might have a deleterious effect. The data presented here also provide new therapy opportunities, whether for creating more effective drugs for diabetes or implementing specific treatment for patients with these mutations.

GENERAL SIGNIFICANCE

Our data could help to better understand the impact of mutations on the wild-type amylin sequence, as a starting point for the evaluation and characterization of other variations. Moreover, these findings could improve the health of patients with type II diabetes.

Optimized expression and purification of a soluble BMP2 variant based on in-silico design

Bone morphogenetic protein 2 (BMP2¹) is a highly interesting therapeutic growth factor due to its strong osteogenic/osteoinductive potential. However, its pronounced aggregation tendency renders recombinant and soluble production troublesome and complex. While prokaryotic expression systems can provide BMP2 in large amounts, the typically insoluble protein requires complex denaturation-renaturation procedures with medically hazardous reagents to obtain natively folded homodimeric BMP2. Based on a detailed aggregation analysis of wildtype BMP2, we designed a hydrophilic variant of BMP2 additionally containing an improved heparin binding site (BMP2-2Hep-7M). Consecutive optimization of BMP2-2Hep-7M expression and purification enabled production of soluble dimeric BMP2-2Hep-7M in high yield in E. coli. This was achieved by a) increasing protein hydrophilicity via introducing seven point mutations within aggregation hot spots of wildtype BMP2 and a longer N-terminus resulting in higher affinity for heparin, b) by employing E. coli strain SHuffle® T7, which enables the structurally essential disulfide-bond formation in BMP2 in the cytoplasm, c) by using BMP2 variant characteristic soluble expression conditions and application of l-arginine as solubility enhancer. The BMP2 variant BMP2-2Hep-7M shows strongly attenuated although not completely eliminated aggregation tendency.

Utilizing Red Spotted Apollo Butterfly Transcriptome to Identify Antimicrobial Peptide Candidates against Porphyromonas gingivalis

Classical antibiotics are the foremost treatment strategy against microbial infections. Overuse of this has led to the evolution of antimicrobial resistance. Antimicrobial peptides (AMPs) are natural defense elements present across many species including humans, insects, bacteria, and plants. Insect AMPs are our area of interest, because of their stronger abilities in host defense. We have deciphered AMPs from an endangered species Parnassius bremeri, commonly known as the red spotted apollo butterfly. It belongs to the second largest insect order Lepidoptera, comprised of butterflies and moths, and lives in the high altitudes of Russia, China, and Korea. We aimed at identifying the AMPs from the larvae stages. The rationale of choosing this stage is that the P. bremeri larvae development occurs at extremely low temperature conditions, which might serve as external stimuli for AMP production. RNA was isolated from larvae (L1 to L5) instar stages and subjected to next generation sequencing. The transcriptomes obtained were curated in in-silico pipelines. The peptides obtained were screened for requisite AMP physicochemical properties and in vitro antimicrobial activity. With the sequential screening and validation, we obtained fifteen candidate AMPs. One peptide TPS-032 showed promising antimicrobial activity against Porphyromonas gingivalis, a primary causative organism of periodontitis.

Noncanonical protein kinase A activation by oligomerization of regulatory subunits as revealed by inherited Carney complex mutations

Familial mutations of the protein kinase A (PKA) R1α regulatory subunit lead to a generalized predisposition for a wide range of tumors, from pituitary adenomas to pancreatic and liver cancers, commonly referred to as Carney complex (CNC). CNC mutations are known to cause overactivation of PKA, but the molecular mechanisms underlying such kinase overactivity are not fully understood in the context of the canonical cAMP-dependent activation of PKA. Here, we show that oligomerization-induced sequestration of R1α from the catalytic subunit of PKA (C) is a viable mechanism of PKA activation that can explain the CNC phenotype. Our investigations focus on comparative analyses at the level of structure, unfolding, aggregation, and kinase inhibition profiles of wild-type (wt) PKA R1α, the A211D and G287W CNC mutants, as well as the cognate acrodysostosis type 1 (ACRDYS1) mutations A211T and G287E. The latter exhibit a phenotype opposite to CNC with suboptimal PKA activation compared with wt. Overall, our results show that CNC mutations not only perturb the classical cAMP-dependent allosteric activation pathway of PKA, but also amplify significantly more than the cognate ACRDYS1 mutations nonclassical and previously unappreciated activation pathways, such as oligomerization-induced losses of the PKA R1α inhibitory function.

Zika virus capsid anchor forms cytotoxic amyloid-like fibrils

Capsid-anchor (CA) of Zika virus (ZIKV) is a small, single-pass transmembrane sequence that separates the capsid (C) protein from downstream pre-membrane (PrM) protein. During polyprotein processing, CA is cleaved-off from C and PrM and left as a membrane-embedded peptide. CA plays an essential role in the assembly and maturation of the virus. However, its independent folding behavior is still unknown. Therefore, in this study, we investigated the amyloid-forming propensity of CA at physiological conditions. We observed the aggregation behavior of CA peptide using dye-binding assays and ThT kinetics. The morphological analysis of CA aggregates explored by high-resolution microscopy (TEM, AFM) and Far-UV CD spectroscopy revealed characteristic amyloid-like fibrils rich in β-sheet secondary structure. Further, the effect on mammalian cells exhibited the cytotoxic nature of the CA amyloid-fibrils. Our findings collectively shed light on the amyloidogenic phenomenon of flaviviral protein, which may contribute to their infection.

Molecular characterization of a novel β-defensin isoform from the red-toothed trigger fish, Odonus niger (Ruppel, 1836)

Background

The concern regarding a post-antibiotic era with increasing drug resistance by pathogens imposes the need to discover alternatives for existing antibiotics. Antimicrobial peptides (AMPs) with their versatile therapeutic properties are a group of promising molecules with curative potentials. These evolutionarily conserved molecules play important roles in the innate immune system of several organisms. The β-defensins are a group of cysteine rich cationic antimicrobial peptides that play an important role in the innate immune system by their antimicrobial activity against the invading pathogens. The present study deals with a novel β-defensin isoform from the red-toothed trigger fish, Odonus niger. Total RNA was isolated from the gills, cDNA was synthesized and the β-defensin isoform obtained by polymerase chain reaction was cloned and subjected to structural and functional characterization in silico.

Results

A β-defensin isoform could be detected from the gill mRNA of red-toothed trigger fish, Odonus niger. The cDNA encoded a 63 amino acid peptide, β-defensin, with a 20 amino acid signal sequence followed by 43 amino acid cationic mature peptide (On-Def) having a molecular weight of 5.214 kDa and theoretical pI of 8.89. On-Def possessed six highly conserved cysteine residues forming disulfide bonds between C1–C5, C2–C4, and C3–C6, typical of β-defensins. An anionic pro-region was observed prior to the β-defensin domain within the mature peptide. Clustal alignment and phylogenetic analyses revealed On-Def as a group 2 β-defensin. Furthermore, it shared some structural similarities and functional motifs with β-defensins from other organisms. On-Def was predicted to be non-hemolytic with anti-bacterial, anti-viral, anti-fungal, anti-cancer, and immunomodulatory potential.

Conclusion

On-Def is the first report of a β-defensin from the red-toothed trigger fish, Odonus niger. The antimicrobial profile showed the potential for further studies as a suitable candidate for antimicrobial peptide therapeutics.

Combining genetic algorithm with machine learning strategies for designing potent antimicrobial peptides

BACKGROUND

Current methods in machine learning provide approaches for solving challenging, multiple constraint design problems. While deep learning and related neural networking methods have state-of-the-art performance, their vulnerability in decision making processes leading to irrational outcomes is a major concern for their implementation. With the rising antibiotic resistance, antimicrobial peptides (AMPs) have increasingly gained attention as novel therapeutic agents. This challenging design problem requires peptides which meet the multiple constraints of limiting drug-resistance in bacteria, preventing secondary infections from imbalanced microbial flora, and avoiding immune system suppression. AMPs offer a promising, bioinspired design space to targeting antimicrobial activity, but their versatility also requires the curated selection from a combinatorial sequence space. This space is too large for brute-force methods or currently known rational design approaches outside of machine learning. While there has been progress in using the design space to more effectively target AMP activity, a widely applicable approach has been elusive. The lack of transparency in machine learning has limited the advancement of scientific knowledge of how AMPs are related among each other, and the lack of general applicability for fully rational approaches has limited a broader understanding of the design space.

METHODS

Here we combined an evolutionary method with rough set theory, a transparent machine learning approach, for designing antimicrobial peptides (AMPs). Our method achieves the customization of AMPs using supervised learning boundaries. Our system employs in vitro bacterial assays to measure fitness, codon-representation of peptides to gain flexibility of sequence selection in DNA-space with a genetic algorithm and machine learning to further accelerate the process.

RESULTS

We use supervised machine learning and a genetic algorithm to find a peptide active against S. epidermidis, a common bacterial strain for implant infections, with an improved aggregation propensity average for an improved ease of synthesis.

CONCLUSIONS

Our results demonstrate that AMP design can be customized to maintain activity and simplify production. To our knowledge, this is the first time when codon-based genetic algorithms combined with rough set theory methods is used for computational search on peptide sequences.

Effect of formulation and peptide folding on the fibrillar aggregation, gelation, and oxidation of a therapeutic peptide

The physical and chemical stability of therapeutic peptides presents challenges in developing robust formulations. The stability of the formulation affects product safety, efficacy and quality. Therefore, an understanding of the effects of formulation variables on the peptide's conformational structure and on its possible physical and chemical degradation is vital. To this end, computational and experimental analysis were employed to investigate the impact of formulation, peptide folding and product handling on oxidation, fibrillar aggregation and gelation of teriparatide. Teriparatide was used as a model drug due to the correlation of its conformation in solution with its pharmacological activity. Fibrillar aggregation and gelation were monitored using four orthogonal techniques. An innovative, automated platform coupled with ion mobility mass spectrometry was used for profiling chemical degradants. Increases in teriparatide concentration, pH, and ionic strength were found to increase the rate of fibrillar aggregation and gelation. Conversely, an increase in peptide folding and stabilization of the folded structures was found to decrease the rate of fibrillar aggregation and gelation. Moreover, the rate of oxidation was found to be inversely related to its solution concentration and extent of peptide folding. The present study provides an insight into formulation strategies designed to reduce the potential risk of physical and chemical degradation of peptides with a defined conformation.

Complementary Structural Information for Stressed Antibodies from Hydrogen-Deuterium Exchange and Covalent Labeling Mass Spectrometry

Identifying changes in the higher-order structure (HOS) of therapeutic monoclonal antibodies upon storage, stress, or mishandling is important for ensuring efficacy and avoiding adverse effects. Here, we demonstrate diethylpyrocarbonate (DEPC)-based covalent labeling (CL) mass spectrometry (MS) and hydrogen-deuterium exchange (HDX)/MS can be used together to provide site-specific information about subtle conformational changes that are undetectable by traditional techniques. Using heat-stressed rituximab as a model protein, we demonstrate that CL/MS is more sensitive than HDX/MS to subtle HOS structural changes under low stress conditions (e.g., 45 and 55 °C for 4 h). At higher heat stress (65 °C for 4 h), we find CL/MS and HDX/MS provide complementary information, as CL/MS reports on changes in side chain orientation while HDX/MS reveals changes in backbone dynamics. More interestingly, we demonstrate that the two techniques work synergistically to identify likely aggregation sites in the heat-stressed protein. In particular, the C_H3 and C_L domains experience decreases in deuterium uptake after heat stress, while only the C_H3 domain experiences decreases in DEPC labeling extent as well, suggesting the C_H3 domain is a likely site of aggregation and the C_L domain only undergoes a decrease in backbone dynamics. The combination of DEPC-CL/MS and HDX/MS provides valuable structural information, and the two techniques should be employed together when investigating the HOS of protein therapeutics.

Comparison of the pH- and thermally-induced fluctuations of a therapeutic antibody Fab fragment by molecular dynamics simulation

Successful development of protein therapeutics depends critically on achieving stability under a range of conditions. A deeper understanding of the drivers of instability across different stress conditions, will enable the engineering of more robust protein scaffolds. We compared the impacts of low pH and high temperature stresses on the structure of a humanized antibody fragment (Fab) A33, using atomistic molecular dynamics simulations, using a recent 2.5 Å crystal structure. This revealed that low-pH induced the loss of native contacts in the domain C_L. By contrast, thermal stress led to 5-7% loss of native contacts in all four domains, and simultaneous loss of >30% of native contacts in the V_L-V_H and C_L-C_H interfaces. This revealed divergent destabilising pathways under the two different stresses. The underlying cause of instability was probed using FoldX and Rosetta mutation analysis, and packing density calculations. These agreed that mutations in the C_L domain, and C_L-C_H1 interface have the greatest potential for stabilisation of Fab A33. Several key salt bridge losses underpinned the conformational change in C_L at low pH, whereas at high temperature, salt bridges became more dynamic, thus contributing to an overall destabilization. Lastly, the unfolding events at the two stress conditions exposed different predicted aggregation-prone regions (APR) to solvent, which would potentially lead to different aggregation mechanisms. Overall, our results identified the early stages of unfolding and stability-limiting regions of Fab A33, and the V_H and C_L domains as interesting future targets for engineering stability to both pH- and thermal-stresses simultaneously.

MILAMP: Multiple Instance Prediction of Amyloid Proteins

Amyloid proteins are implicated in several diseases such as Parkinson's, Alzheimer's, prion diseases, etc. In order to characterize the amyloidogenicity of a given protein, it is important to locate the amyloid forming hotspot regions within the protein as well as to analyze the effects of mutations on these proteins. The biochemical and biological assays used for this purpose can be facilitated by computational means. This paper presents a machine learning method that can predict hotspot amyloidogenic regions within proteins and characterize changes in their amyloidogenicity due to point mutations. The proposed method called MILAMP (Multiple Instance Learning of AMyloid Proteins) achieves high accuracy for identification of amyloid proteins, hotspot localization, and prediction of mutation effects on amyloidogenicity by integrating heterogenous data sources and exploiting common predictive patterns across these tasks through multiple instance learning. The paper presents comprehensive benchmarking experiments to test the predictive performance of MILAMP in comparison to previously published state of the art techniques for amyloid prediction. The python code for the implementation and webserver for MILAMP is available at the URL: http://faculty.pieas.edu.pk/fayyaz/software.html#MILAMP.

Combining Unfolding Reversibility Studies and Molecular Dynamics Simulations to Select Aggregation-Resistant Antibodies

The efficient development of new therapeutic antibodies relies on developability assessment with biophysical and computational methods to find molecules with drug-like properties such as resistance to aggregation. Despite the many novel approaches to select well-behaved proteins, antibody aggregation during storage is still challenging to predict. For this reason, there is a high demand for methods that can identify aggregation-resistant antibodies. Here, we show that three straightforward techniques can select the aggregation-resistant antibodies from a dataset with 13 molecules. The ReFOLD assay provided information about the ability of the antibodies to refold to monomers after unfolding with chemical denaturants. Modulated scanning fluorimetry (MSF) yielded the temperatures that start causing irreversible unfolding of the proteins. Aggregation was the main reason for poor unfolding reversibility in both ReFOLD and MSF experiments. We therefore performed temperature ramps in molecular dynamics (MD) simulations to obtain partially unfolded antibody domains in silico and used CamSol to assess their aggregation potential. We compared the information from ReFOLD, MSF, and MD to size-exclusion chromatography (SEC) data that shows whether the antibodies aggregated during storage at 4, 25, and 40 °C. Contrary to the aggregation-prone molecules, the antibodies that were resistant to aggregation during storage at 40 °C shared three common features: (i) higher tendency to refold to monomers after unfolding with chemical denaturants, (ii) higher onset temperature of nonreversible unfolding, and (iii) unfolding of regions containing aggregation-prone sequences at higher temperatures in MD simulations.

Intestinal guard: Human CXCL17 modulates protective response against mycotoxins and CXCL17-mimetic peptides development

Mycotoxin contamination is an ongoing and growing issue that can create health risks and even cause death. Unfortunately, there is currently a lack of specific therapy against mycotoxins with few side effects. On the other hand, the strategic expression of CXCL17 in mucosal tissues suggests that it may be involved in immune response when exposed to mycotoxins, but the exact role of CXCL17 remains largely unknown. Using Caco-2 as a cell model of the intestinal epithelial barrier (the first line of defense against mycotoxins), we showed that a strong production of ROS-dependent CXCL17 was triggered by mycotoxins via p38 and JNK pathways. Under the mycotoxins stress, CXCL17 modulated enhanced immuno-protective response with a remission of inflammation and apoptosis through PI3K/AKT/mTOR. Based on our observed feedback of CXCL17 to the mycotoxins, we developed the CXCL17-mimetic peptides in silico (CX1 and CX2) that possessed the safety and the capability to ameliorate mycotoxins-inducible inflammation and apoptosis. In this study, the identification of detoxifying feature of CXCL17 is a prominent addition to the chemokine field, pointing out a new direction for curing the mycotoxins-caused damage.

Bioinformatics methods for identification of amyloidogenic peptides show robustness to misannotated training data

Several disorders are related to amyloid aggregation of proteins, for example Alzheimer's or Parkinson's diseases. Amyloid proteins form fibrils of aggregated beta structures. This is preceded by formation of oligomers-the most cytotoxic species. Determining amyloidogenicity is tedious and costly. The most reliable identification of amyloids is obtained with high resolution microscopies, such as electron microscopy or atomic force microscopy (AFM). More frequently, less expensive and faster methods are used, especially infrared (IR) spectroscopy or Thioflavin T staining. Different experimental methods are not always concurrent, especially when amyloid peptides do not readily form fibrils but oligomers. This may lead to peptide misclassification and mislabeling. Several bioinformatics methods have been proposed for in-silico identification of amyloids, many of them based on machine learning. The effectiveness of these methods heavily depends on accurate annotation of the reference training data obtained from in-vitro experiments. We study how robust are bioinformatics methods to weak supervision, encountering imperfect training data. AmyloGram and three other amyloid predictors were applied. The results proved that a certain degree of misannotation in the reference data can be eliminated by the bioinformatics tools, even if they belonged to their training set. The computational results are supported by new experiments with IR and AFM methods.

Atomistic Modeling of Peptide Aggregation and β-Sheet Structuring in Corn Zein for Viscoelasticity

The structure-function relationships of plant-based proteins that give rise to desirable texture attributes in order to mimic meat products are generally unknown. In particular, it is not clear how to engineer viscoelasticity to impart cohesiveness and proper mouthfeel; however, it is known that intermolecular β-sheet structures have the potential to enhance the viscoelastic property. Here, we investigated the propensity of selected peptide segments within common corn α-zein variants to maintain stable aggregates and β-sheet structures. Simulations on dimer systems showed that stability was influenced by the initial orientation and the presence of contiguous small hydrophobic residues. Simulations using eight-peptide β-sheet oligomers revealed that peptide sequences without proline had higher levels of β-sheet structuring. Additionally, we identified that sequences with a dimer hydrogen-bonding density of >22% tended to have a larger percent β-sheet conformation. These results contribute to understanding how the viscoelasticity of zein can be increased for use in plant-based meat analogues.

Potential aggregation hot spots in recombinant human keratinocyte growth factor: a computational study

The recombinant human keratinocyte growth factor (rhKGF) is a highly aggregation-prone therapeutic protein. The high aggregation liability of rhKGF is manifested by loss of the monomeric state, and accumulation of the aggregated species even at moderate temperatures. Here, we analyzed the rhKGF for its vulnerability toward aggregation by detection of aggregation-prone regions (APRs) using several sequence-based computational tools including TANGO, ZipperDB, AGGRESCAN, Zyggregator, Camsol, PASTA, SALSA, WALTZ, SODA, Amylpred, AMYPDB, and structure-based tools including SolubiS, CamSol structurally corrected, Aggrescan3D and spatial aggregation propensity (SAP) algorithm. The sequence-based prediction of APRs in rhKGF indicated that they are mainly located at positions 10-30, 40-60, 61-66, 88-120, and 130-140. Mapping on the rhKGF structure revealed that most of these residues including F16-R25, I43, E45, R47-I56, F61, Y62, N66, L88-E91, E108-F110, A112, N114, T131, and H133-T140 are surface-exposed in the native state which can promote aggregation without major unfolding event, or the conformational change may occur in the oligomers. The other regions are buried in the native state and their contribution to non-native aggregation is mediated by a preceding unfolding event. The structure-based prediction of APRs using the SAP tool limited the number of identified APRs to the dynamically-exposed hydrophobic residues including V12, A50, V51, L88, I89, L90, I118, L135, and I139 mediating the native-state aggregation. Our analysis of APRs in rhKGF identified the regions determining the intrinsic aggregation propensity of the rhKGF which are the candidate positions for engineering the rhKGF to reduce its aggregation tendency.Communicated by Ramaswamy H. Sarma.

The chaperone-binding activity of the mitochondrial surface receptor Tom70 protects the cytosol against mitoprotein-induced stress

Most mitochondrial proteins are synthesized as precursors in the cytosol and post-translationally transported into mitochondria. The mitochondrial surface protein Tom70 acts at the interface of the cytosol and mitochondria. In vitro import experiments identified Tom70 as targeting receptor, particularly for hydrophobic carriers. Using in vivo methods and high-content screens, we revisit the question of Tom70 function and considerably expand the set of Tom70-dependent mitochondrial proteins. We demonstrate that the crucial activity of Tom70 is its ability to recruit cytosolic chaperones to the outer membrane. Indeed, tethering an unrelated chaperone-binding domain onto the mitochondrial surface complements most of the defects caused by Tom70 deletion. Tom70-mediated chaperone recruitment reduces the proteotoxicity of mitochondrial precursor proteins, particularly of hydrophobic inner membrane proteins. Thus, our work suggests that the predominant function of Tom70 is to tether cytosolic chaperones to the outer mitochondrial membrane, rather than to serve as a mitochondrion-specifying targeting receptor.

Evaluation of the roles of hydrophobic residues in the N-terminal region of archaeal trehalase in its folding

Thermoplasma trehalase Tvn1315 is predicted to be composed of a β-sandwich domain (BD) and a catalytic domain (CD) based on the structure of the bacterial GH15 family glucoamylase (GA). Tvn1315 as well as Tvn1315 (Δ5), in which the 5 N-terminal amino acids are deleted, could be expressed in Escherichia coli as active enzymes, but deletion of 10 residues (Δ10) led to inclusion body formation. To further investigate the role of the N-terminal region of BD, we constructed five mutants of Δ5, in which each of the 5th to 10th residues of the N-terminus of Tvn1315 was mutated to Ala. Every mutant protein could be recovered in soluble form, but only a small fraction of the Y9A mutant was recovered in the soluble fraction. The Y9A mutant recovered in soluble form had similar specific activity to the other proteins. Subsequent mutation analysis at the 9th position of Tvn1315 in Δ5 revealed that aromatic as well as bulky hydrophobic residues could function properly, but residues with hydroxy groups impaired the solubility. Similar results were obtained with mutants based on untruncated Tvn1315. When the predicted BD, Δ5BD, Δ10BD, and BD mutants were expressed, the Δ10BD protein formed inclusion bodies, and the BD mutants behaved similarly to the Δ5 and full-length enzyme mutants. These results suggest that the hydrophobic region is involved in the solubilization of BD during the folding process. Taken together, these results indicate that the solubility of CD depends on BD folding. KEY POINTS: • N-terminal hydrophobic region of the BD is involved in the protein folding. • The N-terminal hydrophobic region of the BD is also involved in the BD folding. • BD is able to weakly interact with the insoluble β-glucan.

Comprehensive assessment of machine learning-based methods for predicting antimicrobial peptides

Antimicrobial peptides (AMPs) are a unique and diverse group of molecules that play a crucial role in a myriad of biological processes and cellular functions. AMP-related studies have become increasingly popular in recent years due to antimicrobial resistance, which is becoming an emerging global concern. Systematic experimental identification of AMPs faces many difficulties due to the limitations of current methods. Given its significance, more than 30 computational methods have been developed for accurate prediction of AMPs. These approaches show high diversity in their data set size, data quality, core algorithms, feature extraction, feature selection techniques and evaluation strategies. Here, we provide a comprehensive survey on a variety of current approaches for AMP identification and point at the differences between these methods. In addition, we evaluate the predictive performance of the surveyed tools based on an independent test data set containing 1536 AMPs and 1536 non-AMPs. Furthermore, we construct six validation data sets based on six different common AMP databases and compare different computational methods based on these data sets. The results indicate that amPEPpy achieves the best predictive performance and outperforms the other compared methods. As the predictive performances are affected by the different data sets used by different methods, we additionally perform the 5-fold cross-validation test to benchmark different traditional machine learning methods on the same data set. These cross-validation results indicate that random forest, support vector machine and eXtreme Gradient Boosting achieve comparatively better performances than other machine learning methods and are often the algorithms of choice of multiple AMP prediction tools.

The Budapest Amyloid Predictor and Its Applications

The amyloid state of proteins is widely studied with relevance to neurology, biochemistry, and biotechnology. In contrast with nearly amorphous aggregation, the amyloid state has a well-defined structure, consisting of parallel and antiparallel β-sheets in a periodically repeated formation. The understanding of the amyloid state is growing with the development of novel molecular imaging tools, like cryogenic electron microscopy. Sequence-based amyloid predictors were developed, mainly using artificial neural networks (ANNs) as the underlying computational technique. From a good neural-network-based predictor, it is a very difficult task to identify the attributes of the input amino acid sequence, which imply the decision of the network. Here, we present a linear Support Vector Machine (SVM)-based predictor for hexapeptides with correctness higher than 84%, i.e., it is at least as good as the best published ANN-based tools. Unlike artificial neural networks, the decisions of the linear SVMs are much easier to analyze and, from a good predictor, we can infer rich biochemical knowledge. In the Budapest Amyloid Predictor webserver the user needs to input a hexapeptide, and the server outputs a prediction for the input plus the 6 × 19 = 114 distance-1 neighbors of the input hexapeptide.

Machine Learning Attempts for Predicting Human Subcutaneous Bioavailability of Monoclonal Antibodies

PURPOSE

One knowledge gap related to subcutaneous (SC) delivery is unpredictable and variable bioavailability. This study was aimed to develop machine learning methods to predict whether mAb's bioavailability was ≥70% or below, without completely knowing the mechanism and causality between inputs and outputs.

METHODS

A database of mAb SC products was built. The model training and validation were accomplished based on this database and a set of the inputs (product properties) were mapped to the output (bioavailability) using different machine learning algorithms. Dimensionality reduction was undertaken using principal component analysis (PCA).

RESULTS

The bioavailability of the mAb products being investigated varied from 35% to 90%. The tree-based methods, including random forest (RF), Adaptive Boost (AdaBoost), and decision tree (DT) presented the best predictability and generalization power on bioavailability classification. The models based on Multi-layer perceptron (MLP), Gaussian Naïve Bayes (GaussianNB), and k nearest neighbor (kNN) algorithms also provided acceptable prediction accuracy.

CONCLUSION

Machine learning could be a potential tool to predict mAb's bioavailability. Since all input features were acquired using theoretical calculations and predictions rather than experiments, the models may be particularly applicable to some early-stage research activities such as mAb molecule triage, design/optimization, mutant screening, molecule selection, and formulation design.

Phase Separation and Neurodegenerative Diseases: A Disturbance in the Force

Protein aggregation is the main hallmark of neurodegenerative diseases. Many proteins found in pathological inclusions are known to undergo liquid-liquid phase separation, a reversible process of molecular self-assembly. Emerging evidence supports the hypothesis that aberrant phase separation behavior may serve as a trigger of protein aggregation in neurodegeneration, and efforts to understand and control the underlying mechanisms are underway. Here, we review similarities and differences among four main proteins, α-synuclein, FUS, tau, and TDP-43, which are found aggregated in different diseases and were independently shown to phase separate. We discuss future directions in the field that will help shed light on the molecular mechanisms of aggregation and neurodegeneration.

MAPRE2 mutations result in altered human cranial neural crest migration, underlying craniofacial malformations in CSC-KT syndrome

Circumferential skin creases (CSC-KT) is a rare polymalformative syndrome characterised by intellectual disability associated with skin creases on the limbs, and very characteristic craniofacial malformations. Previously, heterozygous and homozygous mutations in MAPRE2 were found to be causal for this disease. MAPRE2 encodes for a member of evolutionary conserved microtubule plus end tracking proteins, the end binding (EB) family. Unlike MAPRE1 and MAPRE3, MAPRE2 is not required for the persistent growth and stabilization of microtubules, but plays a role in other cellular processes such as mitotic progression and regulation of cell adhesion. The mutations identified in MAPRE2 all reside within the calponin homology domain, responsible to track and interact with the plus-end tip of growing microtubules, and previous data showed that altered dosage of MAPRE2 resulted in abnormal branchial arch patterning in zebrafish. In this study, we developed patient derived induced pluripotent stem cell lines for MAPRE2, together with isogenic controls, using CRISPR/Cas9 technology, and differentiated them towards neural crest cells with cranial identity. We show that changes in MAPRE2 lead to alterations in neural crest migration in vitro but also in vivo, following xenotransplantation of neural crest progenitors into developing chicken embryos. In addition, we provide evidence that changes in focal adhesion might underlie the altered cell motility of the MAPRE2 mutant cranial neural crest cells. Our data provide evidence that MAPRE2 is involved in cellular migration of cranial neural crest and offers critical insights into the mechanism underlying the craniofacial dysmorphisms and cleft palate present in CSC-KT patients. This adds the CSC-KT disorder to the growing list of neurocristopathies.

Dialysis-related amyloidosis associated with a novel β2-microglobulin variant

Till date, there had been no reported case of dialysis-related amyloidosis (DRA) associated with a β₂-microglobulin variant. We report here a 41-year-old haemodialysis patient with systemic amyloidosis, exhibiting macroglossia and swelling salivary glands, uncommon clinical manifestations for DRA. Molecular analysis showed that the patient had a new variant of β₂-microglobulin (V27M). Extracted amyloid protein was predominantly composed of variant β₂-microglobulin. In vitro analysis revealed that this variant β₂-microglobulin had a strong amyloidogenic propensity, probably owing to the decreased stability caused by a bulky methionine residue. Our data clearly show that V27M variant is amyloidogenic and this mutation results in unusual clinical manifestations. To date, only one amyloidogenic β₂-microglobulin variant (D76N) has been reported in non-dialysis patients. It is noteworthy that the V27M and D76N variants show substantial differences in both clinical phenotypes and pathomechanical features. This is the first case of DRA associated with a naturally occurring β₂-microglobulin variant.

De novo design of transmembrane β barrels

Transmembrane β-barrel proteins (TMBs) are of great interest for single-molecule analytical technologies because they can spontaneously fold and insert into membranes and form stable pores, but the range of pore properties that can be achieved by repurposing natural TMBs is limited. We leverage the power of de novo computational design coupled with a "hypothesis, design, and test" approach to determine TMB design principles, notably, the importance of negative design to slow β-sheet assembly. We design new eight-stranded TMBs, with no homology to known TMBs, that insert and fold reversibly into synthetic lipid membranes and have nuclear magnetic resonance and x-ray crystal structures very similar to the computational models. These advances should enable the custom design of pores for a wide range of applications.

Aggregation hot spots in the SARS-CoV-2 proteome may constitute potential therapeutic targets for the suppression of the viral replication and multiplication

The emergence of novel coronavirus SARS-CoV-2 is responsible for causing coronavirus disease-19 (COVID-19) imposing serious threat to global public health. Infection of SARS-CoV-2 to the host cell is characterized by direct translation of positive single stranded (+ ss) RNA to form large polyprotein polymerase 1ab (pp1ab), which acts as precursor for a number of nonstructural and structural proteins that play vital roles in replication of viral genome and biosynthesis of new virus particles. The maintenance of viral protein homeostasis is essential for continuation of viral life cycle in the host cell. To test whether the protein homeostasis of SARS-CoV-2 can be disrupted by inducing specific protein aggregation, we made an effort to examine whether the viral proteome contains any aggregation prone regions (APRs) that can be explored for inducing toxic protein aggregation specifically in viral proteins and without affecting the host cell. This curiosity leads to the identification of several (> 70) potential APRs in SARS-CoV-2 proteome. The length of the APRs ranges from 5 to 25 amino acid residues. Nearly 70% of total APRs investigated are relatively smaller and found to be in the range of 5–10 amino acids. The maximum number of ARPs (> 50) was observed in pp1ab. On the other hand, the structural proteins such as, spike (S), nucleoprotein (N), membrane (M) and envelope (E) proteins also possess APRs in their primary structures which altogether constitute 30% of the total APRs identified. Our findings may provide new windows of opportunities to design specific peptide-based, anti-SARS-CoV-2 therapeutic molecules against COVID-19.

Challenges Associated With the Formation of Recombinant Protein Inclusion Bodies in Escherichia coli and Strategies to Address Them for Industrial Applications

Recombinant proteins are becoming increasingly important for industrial applications, where Escherichia coli is the most widely used bacterial host for their production. However, the formation of inclusion bodies is a frequently encountered challenge for producing soluble and functional recombinant proteins. To overcome this hurdle, different strategies have been developed through adjusting growth conditions, engineering host strains of E. coli, altering expression vectors, and modifying the proteins of interest. These approaches will be comprehensively highlighted with some of the new developments in this review. Additionally, the unique features of protein inclusion bodies, the mechanism and influencing factors of their formation, and their potential advantages will also be discussed.

Monoclonal Antibodies: A Prospective and Retrospective View

BACKGROUND

Monoclonal Antibodies (mAbs) represent one of the most important classes of biotherapeutic agents. They are used to cure many diseases, including cancer, autoimmune diseases, cardiovascular diseases, angiogenesis-related diseases and, more recently also haemophilia. They can be highly varied in terms of format, source, and specificity to improve efficacy and to obtain more targeted applications. This can be achieved by leaving substantially unchanged the basic structural components for paratope clustering.

OBJECTIVES

The objective was to trace the most relevant findings that have deserved prestigious awards over the years, to report the most important clinical applications and to emphasize their latest emerging therapeutic trends.

RESULTS

We report the most relevant milestones and new technologies adopted for antibody development. Recent efforts in generating new engineered antibody-based formats are briefly reviewed. The most important antibody-based molecules that are (or are going to be) used for pharmacological practice have been collected in useful tables.

CONCLUSION

The topics here discussed prove the undisputed role of mAbs as innovative biopharmaceuticals molecules and as vital components of targeted pharmacological therapies.

Cryo-EM reveals structural breaks in a patient-derived amyloid fibril from systemic AL amyloidosis

Systemic AL amyloidosis is a debilitating and potentially fatal disease that arises from the misfolding and fibrillation of immunoglobulin light chains (LCs). The disease is patient-specific with essentially each patient possessing a unique LC sequence. In this study, we present two ex vivo fibril structures of a λ3 LC. The fibrils were extracted from the explanted heart of a patient (FOR005) and consist of 115-residue fibril proteins, mainly from the LC variable domain. The fibril structures imply that a 180° rotation around the disulfide bond and a major unfolding step are necessary for fibrils to form. The two fibril structures show highly similar fibril protein folds, differing in only a 12-residue segment. Remarkably, the two structures do not represent separate fibril morphologies, as they can co-exist at different z-axial positions within the same fibril. Our data imply the presence of structural breaks at the interface of the two structural forms.

Aggregation Prone Regions in Antibody Sequences Raised Against Vibrio cholerae: A Bioinformatic Approach

Background:

Cholera, a diarrheal illness, causes millions of deaths worldwide due to large outbreaks. The monoclonal antibody used as therapeutic purposes of cholera is prone to be unstable due to various factors including self-aggregation.

Objectives:

In this bioinformatic analysis, we identified the aggregation prone regions (APRs) of antibody sequences of different immunogens (i.e., CTB, ZnM-CTB, ZnP-CTB, TcpA-CT-CTB, ZnM-TcpA-CT-CTB, ZnP-TcpA-CT-CTB, ZnM-TcpA, ZnP-TcpA, TcpA-CT-TcpA, ZnM-TcpACT- TcpA, ZnP-TcpA-CT-TcpA, Ogawa, Inaba and ZnM-Inaba) raised against Vibrio cholerae.

Methods:

To determine APRs in antibody sequences that were generated after immunizing Vibrio cholerae immunogens on Mus musculus, a total of 94 sequences were downloaded as FASTA format from a protein database and the algorithms such as Tango, Waltz, PASTA 2.0, and AGGRESCAN were followed to analyze probable APRs in all of the sequences.

Results:

A remarkably high number of regions in the monoclonal antibodies were identified to be APRs which could explain a cause of instability/short term protection of the anticholera vaccine.

Conclusion:

To increase the stability, it would be interesting to eliminate the APR residues from the therapeutic antibodies in such a way that the antigen-binding sites or the complementarity determining region loops involved in antigen recognition are not disrupted.

Title: insoluble proteins catch heterologous soluble proteins into inclusion bodies by intermolecular interaction of aggregating peptides

Background

Protein aggregation is a biological event observed in expression systems in which the recombinant protein is produced under stressful conditions surpassing the homeostasis of the protein quality control system. In addition, protein aggregation is also related to conformational diseases in animals as transmissible prion diseases or non-transmissible neurodegenerative diseases including Alzheimer, Parkinson’s disease, amyloidosis and multiple system atrophy among others. At the molecular level, the presence of aggregation-prone domains in protein molecules act as seeding igniters to induce the accumulation of protein molecules in protease-resistant clusters by intermolecular interactions.

Results

In this work we have studied the aggregating-prone performance of a small peptide (L6K2) with additional antimicrobial activity and we have elucidated the relevance of the accompanying scaffold protein to enhance the aggregating profile of the fusion protein. Furthermore, we demonstrated that the fusion of L6K2 to highly soluble recombinant proteins directs the protein to inclusion bodies (IBs) in E. coli through stereospecific interactions in the presence of an insoluble protein displaying the same aggregating-prone peptide (APP).

Conclusions

These data suggest that the molecular bases of protein aggregation are related to the net balance of protein aggregation potential and not only to the presence of APPs. This is then presented as a generic platform to generate hybrid protein aggregates in microbial cell factories for biopharmaceutical and biotechnological applications.

Protein aggregation: in silico algorithms and applications

Protein aggregation is a topic of immense interest to the scientific community due to its role in several neurodegenerative diseases/disorders and industrial importance. Several in silico techniques, tools, and algorithms have been developed to predict aggregation in proteins and understand the aggregation mechanisms. This review attempts to provide an essence of the vast developments in in silico approaches, resources available, and future perspectives. It reviews aggregation-related databases, mechanistic models (aggregation-prone region and aggregation propensity prediction), kinetic models (aggregation rate prediction), and molecular dynamics studies related to aggregation. With a multitude of prediction models related to aggregation already available to the scientific community, the field of protein aggregation is rapidly maturing to tackle new applications.

Computational design of enzymes for biotechnological applications

Enzymes are the natural catalysts that execute biochemical reactions upholding life. Their natural effectiveness has been fine-tuned as a result of millions of years of natural evolution. Such catalytic effectiveness has prompted the use of biocatalysts from multiple sources on different applications, including the industrial production of goods (food and beverages, detergents, textile, and pharmaceutics), environmental protection, and biomedical applications. Natural enzymes often need to be improved by protein engineering to optimize their function in non-native environments. Recent technological advances have greatly facilitated this process by providing the experimental approaches of directed evolution or by enabling computer-assisted applications. Directed evolution mimics the natural selection process in a highly accelerated fashion at the expense of arduous laboratory work and economic resources. Theoretical methods provide predictions and represent an attractive complement to such experiments by waiving their inherent costs. Computational techniques can be used to engineer enzymatic reactivity, substrate specificity and ligand binding, access pathways and ligand transport, and global properties like protein stability, solubility, and flexibility. Theoretical approaches can also identify hotspots on the protein sequence for mutagenesis and predict suitable alternatives for selected positions with expected outcomes. This review covers the latest advances in computational methods for enzyme engineering and presents many successful case studies.

Breakdown of supersaturation barrier links protein folding to amyloid formation

The thermodynamic hypothesis of protein folding, known as the "Anfinsen's dogma" states that the native structure of a protein represents a free energy minimum determined by the amino acid sequence. However, inconsistent with the Anfinsen's dogma, globular proteins can misfold to form amyloid fibrils, which are ordered aggregates associated with diseases such as Alzheimer's and Parkinson's diseases. Here, we present a general concept for the link between folding and misfolding. We tested the accessibility of the amyloid state for various proteins upon heating and agitation. Many of them showed Anfinsen-like reversible unfolding upon heating, but formed amyloid fibrils upon agitation at high temperatures. We show that folding and amyloid formation are separated by the supersaturation barrier of a protein. Its breakdown is required to shift the protein to the amyloid pathway. Thus, the breakdown of supersaturation links the Anfinsen's intramolecular folding universe and the intermolecular misfolding universe.

Generation and functional characterization of a single-chain variable fragment (scFv) of the anti-FGF2 3F12E7 monoclonal antibody

Single-chain variable fragments (scFvs) are small-sized artificial constructs composed of the immunoglobulin heavy and light chain variable regions connected by a peptide linker. We have previously described an anti-fibroblast growth factor 2 (FGF2) immunoglobulin G (IgG) monoclonal antibody (mAb), named 3F12E7, with notable antitumor potential revealed by preclinical assays. FGF2 is a known angiogenesis-associated molecule implicated in tumor progression. In this report, we describe a recombinant scFv format for the 3F12E7 mAb. The results demonstrate that the generated 3F12E7 scFv, although prone to aggregation, comprises an active anti-FGF2 product that contains monomers and small oligomers. Functionally, the 3F12E7 scFv preparations specifically recognize FGF2 and inhibit tumor growth similar to the corresponding full-length IgG counterpart in an experimental model. In silico molecular analysis provided insights into the aggregation propensity and the antigen-recognition by scFv units. Antigen-binding determinants were predicted outside the most aggregation-prone hotspots. Overall, our experimental and prediction dataset describes an scFv scaffold for the 3F12E7 mAb and also provides insights to further engineer non-aggregated anti-FGF2 scFv-based tools for therapeutic and research purposes.

An "epitomic" analysis of the specificity of conformation-dependent, anti-Aß amyloid monoclonal antibodies

Antibodies against Aß amyloid are indispensable research tools and potential therapeutics for Alzheimer's disease. They display several unusual properties, such as specificity for aggregated forms of the peptide, the ability to distinguish polymorphic aggregate structures, and the ability to recognize generic aggregation-related epitopes formed by unrelated amyloid sequences. Understanding the mechanisms underlying these unusual properties and the structures of their corresponding epitopes is crucial for the understanding why antibodies display different therapeutic activities and for the development of more effective therapeutic agents. Here we employed a novel "epitomic" approach to map the fine structure of the epitopes of 28 monoclonal antibodies against amyloid-beta using immunoselection of random sequences from a phage display library, deep sequencing, and pattern analysis to define the critical sequence elements recognized by the antibodies. Although most of the antibodies map to major linear epitopes in the amino terminal 1 to 14 residues of Aß, the antibodies display differences in the target sequence residues that are critical for binding and in their individual preferences for nontarget residues, indicating that the antibodies bind to alternative conformations of the sequence by different mechanisms. Epitomic analysis also identifies discontinuous, nonoverlapping sequence Aß segments that may constitute the conformational epitopes that underlie the aggregation specificity of antibodies. Aggregation-specific antibodies recognize sequences that display a significantly higher predicted propensity for forming amyloid than antibodies that recognize the monomer, indicating that the ability of random sequences to aggregate into amyloid is a critical element of their binding mechanism.

Antimicrobial and Amyloidogenic Activity of Peptides. Can Antimicrobial Peptides Be Used against SARS-CoV-2?

At present, much attention is paid to the use of antimicrobial peptides (AMPs) of natural and artificial origin to combat pathogens. AMPs have several points that determine their biological activity. We analyzed the structural properties of AMPs, as well as described their mechanism of action and impact on pathogenic bacteria and viruses. Recently published data on the development of new AMP drugs based on a combination of molecular design and genetic engineering approaches are presented. In this article, we have focused on information on the amyloidogenic properties of AMP. This review examines AMP development strategies from the perspective of the current high prevalence of antibiotic-resistant bacteria, and the potential prospects and challenges of using AMPs against infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

Effect of urea concentration on instant refolding of Nuclear Export Protein (NEP) from Influenza-A virus H1N1: A solution NMR based investigation

Nuclear-export-protein (NEP) plays multiple-functions during influenza virus replication-cycle and shows unique pattern of conserved residues, which altogether make NEP a potential target for developing novel anti-influenza drugs. However, the mechanistic structural biology of NEP has not been fully characterized so far owing to its tendency to aggregate in solution. As structural information is important to guide rational drug-discovery process; therefore, procedural optimization efforts are going on to achieve properly folded NEP in sub-millimolar concentrations for solution-NMR investigations. As a first step in this direction, the refolding-cum-aggregation behavior of recombinant-NEP with N-terminal purification-tag (referred here as NEPN) at different urea-concentrations has been investigated here by NMR-based methods. Several attempts were made to refold denatured NEP-N through step-dialysis. However, owing to its strong tendency to aggregate, excessive precipitation was observed at sub-higher levels of urea concentration (5.0 ± 1.0 M). Finally, we used drip-dilution method with 10.5 M urea-denatured NEP-N and were able to refold NEP-N instantly. The amide ¹H dispersion of 3.6 ppm (6.6-10.2 ppm) in the ¹⁵N-HSQC-spectra of instantly refolded NEP-N confirmed the folded state. This successful instant-refolding of NEP-N has been reported for the first-time and the underlying mechanism has been rationalized through establishing the complete backbone-resonance-assignments of NEP-N at 9.7 M urea-denatured state.

Functions of Gle1 are governed by two distinct modes of self-association

Gle1 is a conserved, essential regulator of DEAD-box RNA helicases, with critical roles defined in mRNA export, translation initiation, translation termination, and stress granule formation. Mechanisms that specify which, where, and when DDXs are targeted by Gle1 are critical to understand. In addition to roles for stress-induced phosphorylation and inositol hexakisphosphate binding in specifying Gle1 function, Gle1 oligomerizes via its N-terminal domain in a phosphorylation-dependent manner. However, a thorough analysis of the role for Gle1 self-association is lacking. Here, we find that Gle1 self-association is driven by two distinct regions: a coiled-coil domain and a novel 10-amino acid aggregation-prone region, both of which are necessary for proper Gle1 oligomerization. By exogenous expression in HeLa cells, we tested the function of a series of mutations that impact the oligomerization domains of the Gle1A and Gle1B isoforms. Gle1 oligomerization is necessary for many, but not all aspects of Gle1A and Gle1B function, and the requirements for each interaction domain differ. Whereas the coiled-coil domain and aggregation-prone region additively contribute to competent mRNA export and stress granule formation, both self-association domains are independently required for regulation of translation under cellular stress. In contrast, Gle1 self-association is dispensable for phosphorylation and nonstressed translation initiation. Collectively, we reveal self-association functions as an additional mode of Gle1 regulation to ensure proper mRNA export and translation. This work also provides further insight into the mechanisms underlying human gle1 disease mutants found in prenatally lethal forms of arthrogryposis.

Amyloid-Like Peptide Aggregates

Self-assembly of proteins and peptides into the amyloid fold is a widespread phenomenon in the natural world. The structural hallmark of self-assembly into amyloid fibrillar assemblies is the cross-beta motif, which conveys distinct morphological and mechanical properties. The amyloid fibril formation has contrasting results depending on the organism, in the sense that it can bestow an organism with the advantages of mechanical strength and improved functionality or, on the contrary, could give rise to pathological states. In this chapter we review the existing information on amyloid-like peptide aggregates, which could either be derived from protein sequences, but also could be rationally or de novo designed in order to self-assemble into amyloid fibrils under physiological conditions. Moreover, the development of self-assembled fibrillar biomaterials that are tailored for the desired properties towards applications in biomedical or environmental areas is extensively analyzed. We also review computational studies predicting the amyloid propensity of the natural amino acid sequences and the structure of amyloids, as well as designing novel functional amyloid materials.

PredAmyl-MLP: Prediction of Amyloid Proteins Using Multilayer Perceptron

Amyloid is generally an aggregate of insoluble fibrin; its abnormal deposition is the pathogenic mechanism of various diseases, such as Alzheimer's disease and type II diabetes. Therefore, accurately identifying amyloid is necessary to understand its role in pathology. We proposed a machine learning-based prediction model called PredAmyl-MLP, which consists of the following three steps: feature extraction, feature selection, and classification. In the step of feature extraction, seven feature extraction algorithms and different combinations of them are investigated, and the combination of SVMProt-188D and tripeptide composition (TPC) is selected according to the experimental results. In the step of feature selection, maximum relevant maximum distance (MRMD) and binomial distribution (BD) are, respectively, used to remove the redundant or noise features, and the appropriate features are selected according to the experimental results. In the step of classification, we employed multilayer perceptron (MLP) to train the prediction model. The 10-fold cross-validation results show that the overall accuracy of PredAmyl-MLP reached 91.59%, and the performance was better than the existing methods.

Conformational Ensembles of Antibodies Determine Their Hydrophobicity

A major challenge in the development of antibody biotherapeutics is their tendency to aggregate. One root cause for aggregation is exposure of hydrophobic surface regions to the solvent. Many current techniques predict the relative aggregation propensity of antibodies via precalculated scales for the hydrophobicity or aggregation propensity of single amino acids. However, those scales cannot describe the nonadditive effects of a residue’s surrounding on its hydrophobicity. Therefore, they are inherently limited in their ability to describe the impact of subtle differences in molecular structure on the overall hydrophobicity. Here, we introduce a physics-based approach to describe hydrophobicity in terms of the hydration free energy using grid inhomogeneous solvation theory (GIST). We apply this method to assess the effects of starting structures, conformational sampling, and protonation states on the hydrophobicity of antibodies. Our results reveal that high-quality starting structures, i.e., crystal structures, are crucial for the prediction of hydrophobicity and that conformational sampling can compensate errors introduced by the starting structure. On the other hand, sampling of protonation states only leads to good results when combined with high-quality structures, whereas it can even be detrimental otherwise. We conclude by pointing out that a single static homology model may not be adequate for predicting hydrophobicity.

Seeding Brain Protein Aggregation by SARS-CoV-2 as a Possible Long-Term Complication of COVID-19 Infection

Postinfection complications of coronavirus disease 2019 (COVID-19) are still unknown, and one of the long-term concerns in infected people are brain pathologies. The question is that severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection may be an environmental factor in accelerating the sporadic neurodegeneration in the infected population. In this regard, induction of protein aggregation in the brain by SARS-CoV-2 intact structure or a peptide derived from spike protein subunits needs to be considered in futures studies. In this paper, we discuss these possibilities using pieces of evidence from other viruses.

Predicted aggregation-prone region (APR) in βB1-crystallin forms the amyloid-like structure and induces aggregation of soluble proteins isolated from human cataractous eye lens

The aggregation of β-crystallins in the human eye lens constitutes a critical step during the development of cataract. We anticipated that the presence of Aggregation-Prone Regions (APRs) in their primary structure, which might be responsible for conformational change required for the self-assembly. To examine the presence of APRs, we systematically analyzed the primary structures of β-crystallins. Out of seven subtypes, the βB1-crystallin found to possess the highest aggregation score with 9 APRs in its primary structure. To confirm the amyloidogenic nature of these newly identified APRs, we further studied the aggregation behavior of one of the APRs spanning from 174 to 180 residues (¹⁷⁴LWVYGFS¹⁸⁰) of βB1-crystallin, which is referred as βB1_(174-180). Under in vitro conditions, the synthetic analogue of βB1_(174-180) peptide formed visible aggregates and displayed high Congo red (CR) bathochromic shift, Thioflavin T (ThT) binding and fibrilar morphology under transmission electron microscopy, which are the typical characteristics of amyloids. Further, the aggregated βB1_(174-180) was found to induce aggregation of the soluble fraction of proteins isolated from the human cataractous lens. This observation suggests that the presence of APRs in βB1-crystallin might be serving as one of the intrinsic supplementary factors responsible for constitutive aggregation behavior of βB1-crystallin and development of cataract.

ANuPP: A Versatile Tool to Predict Aggregation Nucleating Regions in Peptides and Proteins

Short aggregation prone sequence motifs can trigger aggregation in peptide and protein sequences. Most algorithms developed so far to identify potential aggregation prone regions (APRs) use amino acid residue composition and/or sequence pattern features. In this work, we have investigated the importance of atomic-level characteristics rather than residue level to understand the initiation of aggregation in proteins and peptides. Using atomic-level features an ensemble-classifier, ANuPP has been developed to predict the aggregation-nucleating regions in peptides and proteins. In a dataset of 1279 hexapeptides, ANuPP achieved an area under the curve (AUC) of 0.831 with 77% accuracy on 10-fold cross-validation and an AUC of 0.883 with 83% accuracy in a blind test dataset of 142 hexapeptides. Further, it showed an average SOV of 48.7% on identifying APR regions in 37 proteins. The performance of ANuPP is better than other methods reported in the literature on both amyloidogenic hexapeptide prediction and APR identification. We have developed a web server for ANuPP and it is available at https://web.iitm.ac.in/bioinfo2/ANuPP/. Insights gained from this work demonstrate the importance of atomic and functional group characteristics towards diversity of atomic level origins as well as mechanisms of protein aggregation.

Algorithmic assessment of missense mutation severity in the Von-Hippel Lindau protein

Von Hippel-Lindau disease (VHL) is an autosomal dominant rare disease that causes the formation of angiogenic tumors. When functional, pVHL acts as an E3 ubiquitin ligase that negatively regulates hypoxia inducible factor (HIF). Genetic mutations that perturb the structure of pVHL result in dysregulation of HIF, causing a wide array of tumor pathologies including retinal angioma, pheochromocytoma, central nervous system hemangioblastoma, and clear cell renal carcinoma. These VHL-related cancers occur throughout the lifetime of the patient, requiring frequent intervention procedures, such as surgery, to remove the tumors. Although VHL is classified as a rare disease (1 in 39,000 to 1 in 91,000 affected) there is a large heterogeneity in genetic mutations listed for observed pathologies. Understanding how these specific mutations correlate with the myriad of observed pathologies for VHL could provide clinicians insight into the potential severity and onset of disease. Using a select set of 285 ClinVar mutations in VHL, we developed a multiparametric scoring algorithm to evaluate the overall clinical severity of missense mutations in pVHL. The mutations were assessed according to eight weighted parameters as a comprehensive evaluation of protein misfolding and malfunction. Higher mutation scores were strongly associated with pathogenicity. Our approach establishes a novel in silico method by which VHL-specific mutations can be assessed for their severity and effect on the biophysical functions of the VHL protein.